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dc.contributor.authorSilva Ramírez, Esther Lydia 
dc.contributor.authorLópez Coello, Manuel 
dc.contributor.authorPino-Mejías, Rafael
dc.contributor.otherIngeniería Informáticaes_ES
dc.date.accessioned2025-01-03T17:52:42Z
dc.date.available2025-01-03T17:52:42Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10498/34223
dc.description.abstractThis chapter presents studies about the data imputation to estimate missing values, and the Data Editing and Imputation process to identify and correct values erroneously. Artificial Neural Networks and Support Vector Machines are trained as Machine Learning techniques on real and simulated data sets obtaining a complete data set what help to improve the quality of the variables that define the official indicators of the eight Millennium Development Goals.es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceSoft Computing for Sustainability Sciencees_ES
dc.titleAn Application Sample of Machine Learning Tools, Such as SVM and ANN, for Data Editing and Imputationes_ES
dc.typebook partes_ES
dc.rights.accessRightsclosed accesses_ES
dc.identifier.doi10.1007/978-3-319-62359-7_13
dc.type.hasVersionVoRes_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
This work is under a Creative Commons License Attribution-NonCommercial-NoDerivatives 4.0 Internacional